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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 2061–2070 of 2226 papers

TitleStatusHype
USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MRCode0
Tractable Function-Space Variational Inference in Bayesian Neural NetworksCode0
TRADE: Transfer of Distributions between External Conditions with Normalizing FlowsCode0
Trading Information between Latents in Hierarchical Variational AutoencodersCode0
On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization GuaranteesCode0
On Divergence Measures for Bayesian PseudocoresetsCode0
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental DesignCode0
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net ModelsCode0
On Kalman-Bucy filters, linear quadratic control and active inferenceCode0
Bayesian inference of mixed Gaussian phylogenetic modelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified